AI and Photography; beauty lies in the programmers’ eyes

Can AI and Photography work together?

A few weeks ago, in our article “Music and Artificial Intelligence”, we talked about how technology is modifying the music industry.

We stressed the non-dangerousness of this phenomenon, provided that the application of Artificial Intelligence is carried out with responsibility of all the actors involved.

This speech can also be translated into the relationship between AI and Photography, with the required differences.

There are two cases, opposite in terms of aesthetic and ethical quality, to be taken as an example of the role played by technology in the photography field: the "Dreams of New York" project and the development, in the Machine Learning world, of the GANs technique.

The first one is an artistic project created by Tanner Woodbury and Nikolos Killian, two American designers. While wandering in the streets of New York at the slow pace of Google Street View, they noticed the beauty of some sights of the city.

And that’s why they decided to carry out a photographic project, taking up and turning those “amateur” shots into black and white. They made an exhibition, with an artbook that quickly went sold-out.

The technological tool suddenly became an involuntary art photographer. The role of the American copyright legislation was crucial for the success of the project because, if it is a machine that takes the picture, then the intellectual property belongs to everyone.

On the other hand, GANs’ case was a whole other story.

The acronym stands for Generative Adversarial Networks and indicates a Machine Learning technology invented only in 2014.

Its processes are very simple; there are two neural networks, a generative one and a discriminative opponent. The first one has the task to take data and modify them.

The latter analyzes the results produced by its twin to check if the are within the truthfulness parameters set by the programmer are respected. 

Let's take a practical example: GANs has to analyze a database of thousands of people's faces. The generative neural network has the task to create the image of a completely new face, while the opposing neural network has to find out whether the image created by its companion is real or not. Each image is a battle between the two networks; one wins and the other one loses. The system obviously learns from the outcome of the process.

After a few years of testing, today there are GANs that are able to "imagine" and create faces that are so credible as to be unrecognizable both to the opposing network and to the human eye.

As for Deep Fake, the risk is to see these tools falling into the wrong hands, and perhaps damaging the community by creating non-existent people. Moreover, Copyright allows everyone to use the images of the GANs for their own purposes, precisely because they are created by machines, not by people.

A more philosophical question persists.

Photography is the instrument, maybe the most centered, to tell the story of humanity and reality. If you use it to generate something that does not exist, then a contradiction is created.

Broadly speaking, it is the same critical issue that emerged with the spread of Photoshop, but made more acute by the central role of the machine in the falsification process.

In this case, as well as in similar situations, the problem is not due to technology, but to those who hide behind it. In fact, the GANs had originally been conceived by its creator, Ian Goodfellow, to make large amounts of data available to small researchers and specialized centers, to make AI training more sustainable.

For example, the GANs can create, using a limited database of images, new original elements with which artificial intelligence can be trained, eliminating the cost of retrieving photographs.

Ergo: a tool for the democratization of technology and creativity.

GANs was then used in an extremely creative way, conceptually overcoming its natural purpose. For example, in unique artistic projects such as the "artificial" design of a painting that was sold at an auction for 432 thousand euros.

At the same time, some artists such as the English Anna Ridler, have used GANs in their works and performances; to be mentioned, the short film Fall of the House of Usher, in which design becomes plastic art conceived and composed by the machine.

If we wanted to define the different uses of the GANs, we should distinguish between two intents: creative and "astute". The mentality and objectives of those behind the computer, rather than behind the camera, decide the truthfulness and the ethics of the results. Photography is a science, and today, in the era of numbers, it is more evident than ever.

Photo by Rayan Almuslem on Unsplash

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What is Psychographics? An overview and the User Insight practical case

What is Psychographics?

It is the study of the individual based on his interests, personality, and habits. It is the natural evolution of profiling through socio-demographic, geographical and behavioral data.

Psychographics is not a recent field of study: as a branch of psychology, it was developed and applied to marketing and traditional research (focus groups, market research, etc.).

However, it was through digital technology that it developed its full potential.

By analyzing user behaviors on social media, E-Commerces and any "virtual" environment, Psychographics is now able to profile users in a way that was unthinkable just a few years ago.

Its goal is to understand individual characteristics such as emotions, values​​, and attitudes, as well as a whole other set of psychological factors.

All these data provide precious insights about the motivations behind people's behavior, for example, why they buy a specific product, or support a certain cause, or vote for a particular political candidate.

We all heard about the sadly known Cambridge Analytica accident. The researchers and marketers involved were able to boost numerous political campaigns thanks to illegally retrieved psychographic data from people's social profiles.

The method they used was to divide the subjects into five macro-clusters, based on whether they showed presented or not one of the following psychological traits, namely:

Openness: this trait indicates how open-minded a person is. A person with a high level of openness is curious, creative and open to change.

Consciousness: a person who shows a high level of consciousness is responsible, sets long-term goals and does not act impulsively.

Extroversion: the subjects characterized by this trait love to have fun with people and live in social environments. They are also enthusiastic, but often let themselves be guided by others. They also love being in the center of attention.

Agreeableness: a person with high levels of agreeableness is usually friendly, kind and diplomatic. He also shows optimism and tend to trust the others. 

Emotional stability (or its negative counterpart, Neuroticism): a person with a high level of emotional stability who tends to easily experience positive emotions.

This model, which you can find outlined below, is known as OCEAN (the first letters of the psychographic categories), or BigFive.

How does this model apply to marketing?

Through Psychographics, it is possible to understand the fundamental individual characteristics of your customers, in order to collect useful guidelines on how to communicate and create one-to-one messages. 

Let's make an example. A company that works in the energy market needs to communicate a promotional offer to its public, but first it decides to cluster it with the OCEAN psychographic model.

How would individual communication change?

If the customer shows a strong affinity to the Openness cluster, he will receive a graphically creative banner that offers the possibility to customize the energy contract according to his needs.

Elseways, if the customer belongs to the Extroversion cluster, he will be told that the offer has been appreciated by many people, giving him the possibility to receive a discount if the customer brings a friend.

Given the power that this method makes available to companies, the market has been subject to strict regulations. What Cambridge Analytica did just a few years ago would be impossible to accomplish today. In recent years, alternative tools have been developed, fully compliant with GDPR, which allows companies to acquire the same type of information and to use them - this time - for the benefit of people.

This is why Neosperience has created User Insight, a tool that uses the latest Artificial Intelligence, Machine Learning and Advanced Analytics technologies to allow companies to learn about the psychographic traits of customers, thanks to the analysis of their browsing behaviors.

In a market where the personalization of the offer has become the key of success of commercial proposals, understanding the needs and desires of each customer in full respect of its privacy becomes an essential factor.

The future belongs to those who will be able to use new technologies to constantly improve customer experience, progressively reducing the "gaps" between physical and digital worlds. At Neosperience, we believe that this can be possible, and we work to give substance to a technology that allows companies to be more and more empathic and closer to their customers.

Photo by Markus Spiske on Unsplash

 

Music and Artificial Intelligence. Please don’t shoot the piano player

 

Artificial Intelligence is becoming increasingly widespread, even in unexpected areas. Until a few years ago it was thought that its use would be limited to industrial production, repetitive tasks and, in general, jobs that do not enrich the human spirit. Today this assumption is no longer valid. Now AI is also an artist.

Painting, sculpture, poetry, photography, cinema; there is no artistic field in which Artificial Intelligence has not been applied at least once, often with surprising results.

The musical field is most involved in this revolution of creativity, probably because music, after all, is an art that lives on mathematics and physics, therefore predisposed to the influence of algorithms, codes, and data.

The latest album publications, soundtracks and songs by artists or companies that have used some tools based on Artificial Intelligence have terrorized the music market. According to the experts, today the sector risks a profound revolution (if not destruction). But is it so? In other words, is it right or not to shoot the..."artificial" piano player? 

 

How does the application of artificial intelligence to music work?

We can simply say that, in order to learn, AI is fed thousands and thousands of songs through neural networks (mathematical models that imitate biological neural networks) that work through machine learning, and in particular through deep learning (a sub-category of ML that is also able to infer meanings without human supervision). These pieces are fragmented and studied, and the machine manages to extract the basic information and can recognize the patterns it can use to create original works, similar to those that any artist could compose.

 

Everything depends on the use made of it, and how it sounds…

If the learning process is similar for any system based on machine learning, there are however two different applications of AI for music: Flow Machines by Sony and Magenta by Google, for example, are placed at two extremes.

The first is not a creative Artificial Intelligence, or at least not in the sense in which we assume the term; it merely facilitates the artist's work, allowing the person to free their creativity, stimulating it with suggestions and ideas based on their preferences and attitudes.

Magenta, on the other hand, is a true artificial composer that, depending on the inputs provided to it, independently manages to create an original track. The quality of the composition is still not pleasing from many points of view, but technological innovation is growing exponentially and so are its results.

These are not the only two tools available at the moment; among others, we can mention AIVA, MuseNet of OpenAI, Amper and Jukedeck. Everyone is specialized in some features and functionalities. What they have in common is the fact that they have attracted the attention of media and investors.

If we also consider the recommendation algorithms of streaming platforms like Spotify or Apple Music, or all the applications of AI in the field of editing tools, it is clear that the penetration of this technology in the musical field is more advanced than we might believe.

 

But what are the possible consequences of a macro-spread?

At least in the short term, there should be no substantial change in the way we listen to or choose our music.

Some "artificial" songs and albums, like "I AM AI" by Taryn Southern, sung by the performer but composed, played and produced by the open-source software Amper, will continue to come out and will surely get a good commercial success, but they will be exceptions, and probably they will be appreciated for their innovativeness and not for their intrinsic quality.

Over time, however, things will change. A sign of this evolution is Jukedeck's acquisition, which we mentioned earlier as one of the best intelligent music composition tools, by TikTok, one of the most successful social networks of the last period and especially loved by the new generations.

Imagine what could come of this marriage. Maybe we will have the opportunity, once registered on that social network, to create our song, helped by an evolved AI, and to sing it and share it with friends. 

This way, it would be possible to break down the barrier, impassable for most of people, of learning a musical instrument.

 

Every subscriber could become a singer, a musician, and maybe a music influencer.

This story is the fruit of our imagination, no matter how beautiful or frightening it may be. Things are undoubtedly changing, and music is facing many transformations stimulated by technological innovation (augmented reality concerts, artists who are no longer alive returning to sing in the form of holograms, bitcoins to buy songs and albums directly from singers...and so on).

Ultimately, to answer the question that we asked ourselves in the beginning: is it right or not to shoot the "artificial" piano player? 

Well, there is one thing that is always true: blocking innovation is counterproductive. The goal is to be able to guide it on the right path, to allow a gentle transformation for artists and experts and not damage anyone.

Artificial intelligence is born as a tool to enable or facilitate human activities. In this case, if we know how to use it properly, it could stimulate people's creativity, finally giving shape to art for everyone.

Photo by bady qb on Unsplash

Crisis Management: how the AI can take part on it successfully

In a progressively dynamic, technological and globalized world, companies may encounter more and more potential or real crises. Just think about cyberattacks: in recent years they have multiplied in every field, putting sensitive data and IT systems at serious risk. 

It’s become essential to be able to foresee and solve the problems affecting your brand reputation. If you develop the right skills within your company and you provide yourself the expertise, you’ll be ready to deal with every situation.

On the other hand, if even small problems affect your work, then your business risks to go belly up if an internal or external event occurs.

Recalling the admonition of Ian Mitroff, perhaps the most influential crisis management expert: "You don't have to ask if a critical event will happen but when, where and with what consequences".

Deloitte’s innovative research

In 2018 Deloitte conducted an innovative research on the perception of managers on crisis management. The results were surprising.

What the examiners noticed was a managers' strong self-confidence, as they often think their company will be ready to face unexpected and dangerous events, whereas many of them do not have empirical data to confirm their convictions.

For example, 88% of respondents said they could cope with a corporate scandal, while only 17% had ever experienced it personally or during a simulation.

That's the point. When experiencing a crisis directly, managers' perception changes considerably. 

The research showed that among those who had experienced a dramatic business situation in the two previous years, the need to invest in prevention and training was considerably higher than the priorities highlighted by the colleagues who hadn’t faced a crisis yet.

So what’s the right thing to do?

Getting ready for your business in advance. First of all, it is necessary to draw up a list of possible problems that the company may face. Framing a consistent risk assessment is the first step towards not being caught unprepared. 

Subsequently, a task force should be appointed and organized. It is quite common to involve the subjects that deal with public relations, the highest management of the company, which will have to be the subjects media and institutions will interact with. Moreover, the legal department will have to unravel and explain potential issues within the law.

It is also essential to plan crisis simulations based on the risk assessment previously prepared. Experience is the only useful tool to deal with a crisis in the best possible way, but it is also the magnifying glass on corporate weaknesses.

It says that a person shows his true nature when he’s in danger. The same happens with companies. We must never underestimate the power of simulation for the growth and consciousness of employees and managers.

What are the existing tools for crisis management?

One to be mentioned is In Case of Crisis by RockDove Solutions, an App available for company executives. This device promises to help companies to deal with crises using operational protocols, intra-App messaging systems, customized notifications and activity reports.

However, the real question is: what more can we do?

It is interesting to start from the well-known definition that Timothy Coombs, Associate Professor in Communication Studies at Eastern Illinois University, made of corporate crisis. 

"A crisis is the perception of a not predictable event that endangers the expectations of stakeholders, and that can seriously compromise the operational capacity of an organization with negative consequences".

We will try to identify which solutions, based on AI, would make this definition obsolete.

AI-based solutions 

Let's start with the unpredictability.

We have already seen that the possibility of forecasting risky situations grows - considerably - when managers are properly trained and equipped with the right tools. Now, imagine that we can implement within the tool an AI able to help those responsible for assessing risks, recognizing operations and company size, its geographical position and external macro factors that could influence processes.

Once this has been done, the app's own AI could develop ad hoc training programs for each manager, imagining plausible situations and implications, even on a probabilistic basis, and independently linking questions to best practices and behaviours to be adopted.

Moreover, it could simulate a real crisis, involving all managers at the same time, measuring reaction times and effectiveness of choices and operations, relying on crisis already solved.

Concerning the losses of the company's operational capabilities, its benefits would be to facilitate communication between every member of the task force and to keep crisis parameters under control.

To be more specific, when a crisis is undoubtedly in progress, those who are aware of it could send an alert to all the other managers, with the most crucial data provided by the system.

Specific functions

Furthermore, Artificial Intelligence could keep track of company-related keywords on the Internet and social media, like any other web listening tool, to keep the crisis evolution under control. It is also possible to monitor the work of customer service and task force members in one place.

This way, the risks of seeing business operations compromised would be significantly reduced, thanks to these new technological opportunities. Besides, the AI would learn from its own and others' mistakes, continuously improving and limiting the unfavorable consequences of the crisis.

In conclusion, the possibilities for further improvement exist and must be pursued. Predicting a crisis and limiting its damage is an issue that concerns the life and work of thousands and thousands of workers and citizens. Artificial Intelligence precisely serves this purpose: to help humans live better and more safely.

The dark side of tech’s ethics

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Our judgment, like a pendulum, continuously swings between optimism and pessimism. This inclination is self-evident when we discuss the technological developments, occurred in the last decades, that have modified our way of living. 

In 1964, Umberto Eco published Apocalypse Postponed, an essay that was meant to put in order the different judgments expressed on mass society. Eco tried to find a correct and rational middle ground between those who were enthusiastic about cultural innovations and those who loathed them. As an old catchphrase says, "in medio stat virtus".

 

The current situation

The same arguments could be shifted to our troubled years, where two opposite parties are fighting over different topics such as social networks, privacy, personal relationships, online hate, irresponsibility, and so on. Those who have faith in the birth of a just world and those who predict the end of our existence. As the pendulum above, we feel different emotions about the future of technology.

Recently, media have spread some news about the racist, discriminating and insensitive behavior of Artificial Intelligence's applications. This is usually a matter of social network management, recruitment procedures, predictive policing. 

Well, there is no wonder; technology is not neutral.

Technology is created by humans for humans and carries within it all the prejudices and personal histories of those who develop it. It clearly appears in applications where technology has a voice and relates directly to its creators.

 

Microsoft's Tay bot

In 2016, Microsoft released on Twitter its most advanced bot: Tay, to improve its conversational skills in the social network. In less than 24 hours, Tay started using an offensive and racist language, forcing Microsoft to shut it down. 

The causes of this media disaster were soon discovered: during that short time, the Artificial Intelligence, which wasn't given an understanding and limitation of misbehavior, learned from Twitter users to use inappropriate language.

 

Youtube's moderation system 

Another example to be mentioned for its pervasive presence in our lives is the social networks' moderation system. As we all know, in 90% of cases, an Artificial Intelligence that is trained to recognize inappropriate contents will control users' posts. Well, it is not uncommon that users have been the target of discriminatory censorship performed by the moderation system.

It is interesting to mention the episode involving YouTube, which has penalized, economically and publicly, the LGBTQ themed contents of numerous creators. In this case, the system was not able to distinguish between sexually explicit themes and videos that show the authors' sexual and gender orientations.

Many cases could be mentioned as examples, and many others that have not received media visibility and, thus, remain unclosed. 

 

OpenAI and university courses

However, in recent years, many subjects have understood the importance of this topic. OpenAI , a non-profit company that sees Elon Musk and Bill Gates among its investors, has set itself the goal of creating a free and secure Artificial Intelligence, to improve the life of all humanity, without discrimination.

Many universities, on the other hand, began to develop, within their training offers, examinations and specializations concerning ethics in Artificial Intelligence. Harvard, Stanford and the Massachusetts Institute of Technology among others. All the most important pools of talent in the technology field have finally understood the importance of teaching their students this kind of technology, which is not neutral and must be conceived according to our conscience.

Ultimately, there is only one keystone. Machines don't care about our future; our wellness depends solely on people who develop them.

 

Photo by Nadine Shaabana on Unsplash

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May 24 and 25, 2018, Amsterdam, The Netherlands: the future of technology was there, at the TNW Conference 2018, the award-winning 2-day European festival dedicated to innovation, marketing, communication, and creativity.

With 19 tracks of content, a huge variety of topics was covered: Artificial Intelligence, Machine Learning, and Deep Learning changing companies' businesses; Design thinking transforming our work and helping us solving complex problems: new Marketplaces growing retailers' e-commerce exponentially; Virtual and Augmented Reality making physical and digital objects coexist simultaneously; and many others.

In this wide range of specialties, what are the key insights for the digital experience leaders? Here are the three main trends we have observed.

Artificial Intelligence will turn into Emotional Intelligence
Opening the 'Machine: Learners' track, Cassie Kozyrkov, Chief Decision Scientist at Google, shares her thoughts on the decision intelligence engineering, the emerging discipline that focuses on using ML and AI to improve companies’ businesses.

In a statement, she has captured the attention of the entire audience: 2030 will be the age of emotional intelligence. The Human-AI symbiosis that will take place in the next years will shape the way brands connect with customers across all digital and physical touchpoints, making their relationship closer, personal and intimate.

That will become possible thanks to the ability for Machine Learning and Deep Learning to foster and advance brands' social skills, enabling them to change their communication style depending on what customers’ emotions and reactions are.

If the customer is in a hurry and impatient, or anxious and stressed out, brands will be ready to deliver a different experience than if s/he's calm and relaxed; just like a good seller does when dealing with customers in the store.

Context-aware Artificial Intelligence unlocks the power of Customer Experience
In a world where customer expectations are constantly evolving, 89% of companies believe that customer experience will be their primary basis for competition (Gartner, 2015). That is how Adrian McDermott, President of Products at Zendesk, started what has been one of the most eye-opening speeches of the event.

Artificial Intelligence solutions can help companies to increase customer satisfaction by providing:

- Automation, which removes repetitive work - think of an answer bot instead of a customer service professional).
- Recommendation, that uses content cues to inform decisions customers make - by offering, for example, the right information and help at the right moment.
- Prediction, able to spot trends that humans can’t see - such the expected customer satisfaction, the probability that a customer will become loyal to your brand, or that s/he will recommend your product to others.

Over the coming years, these three AI-based levers will allow leading companies to:

- Embrace a people-first approach, which means, capturing the customer behind the analytics and beyond purely objective data such as demographics.
- Adopt a growth mindset, by figuring out what their customer segments look like and A/B testing what kind of interactions they should activate across those segments.
- Deliver seamless omnichannel experiences and context-based conversations with customers, to close the gap with customers' habits and make them live comprehensive shopping experiences.

Digital communication will move to dialogue
By 2020, the average person will have more conversation with their bot than with their spouse (Gartner, 2016). What is certain is that, within the next few years, having a bot in your app and website will go from being an optional nice-to-have to an essential must-have.

If misdesigned, however, you’ll have a frustrating user interface that will drive your customers away, explains Purna Virji, Senior Manager of Global Engagement at Microsoft. Convinced that we can do much better than state of the art, she reveals us the key principles of designing conversational AI; those that she calls the "4 C's":

A. Clarity.
Mind your language, create a conversational flow and see what sounds natural. To avoid "robotic" perceptions, write for the ear and not for the eye, as the right words to create engagement and trust are not those beautiful to read but those that are nice to hear.

B. Character.
People prefer a virtual agent with an easy-to-perceive personality: it can be warm, formal, or even funny ... For example, if a customer says “thank you” at the end of a conversation, a professional bot will reply “you’re welcome,” while a more empathic bot can answer “you bet!”, and a very friendly one can say “no prob.”

But be careful: do not fall into the trap of turning the bot into a fake human. The goal isn’t for the customer to think they’re talking to a real person, so it’s best if the bot is easy to get to know, with a specific personality, but still clearly a bot.

C. Compassion.
Stepping into your customers’ shoes and making your user interface better understand and resonate with them is probably the most struggling point for today's bots. Think, for example, of their common reactions to small talk.

Even though encountering small talk is pretty common for a bot, that's where conversation often breaks. Quite simply, if a customer says "tks" instead of "thanks" it is pretty common to see the bot reply "Sorry. I do not understand”. Thus, building small talk scenarios becomes essential to avoid the embarrassing “Sorry I don’t understand.”

D. Correction.
There are lots of ways to correct an error without having to say "Sorry." One possible strategy, which also promotes sales, is to offer alternatives: if a customer asks for ordering red tulips, but these are unfortunately out of stock, instead of saying "Sorry, we are out of stock of red tulips" the bot can reply "We’re out of red tulips, would you like yellow or orange tulips instead?". After all, is that not what a good seller would do?

To conclude, this year's edition of the TNW Conference has given us significant insights that we can bring to the Digital Customer Experience environment. If “the world is machine readable,” as stated by Kevin Kelly, Co-founder of WIRED, during his compelling speech, we can add that it should be the same for customers, and for the way they think, feel and behave towards brands.

But - citing McDermott's words - “Oil has no value as you can’t extract energy from it. The same is for data. They have no value as you can’t extract knowledge from them.

That is why companies need to learn how to use Artificial Intelligence solutions to understand who their customers truly are, and thus build better products and experiences, designed for humans.

Download The 7 Pillars Of The New Customer Loyalty to define the foundations on which to build your engagement and loyalty strategy, create innovative experiences and establish a lasting and valuable relationship with your customers.

MIT Predicts The 10 Breakthrough Technologies For 2018

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When you talk about the future of technology, you have two different approaches. The first one is to look into the distance the way Sci-Fi writers do, working with the imagination to push the boundaries of what the human mind can create.

The second approach is to aim at a closer target, looking at what is already going on. This is exactly what the MIT does with its annual prediction of the 10 breakthrough technologies that will lead the evolution of business and society, starting from today.

The list has been published in the recent March/April release of the MIT Technology Review magazine, the reference point for everyone interested in knowing what’s coming next. This top 10 includes the technologies believed to make the most impact over the next 12 months.

"This is our attempt to alert our readers: These are the technologies that you really need to or should pay attention to next year, and also going into the next few years," MIT Tech Review's editor David Rotman told Business Insider.

What does it mean ‘breakthrough’? Scrolling through the previous 17 editions of the list, you can find a few key benchmarks for defining this term: mass commercial use, foreseeable mass commercial adoption and, most of all, the profound impact on our lives.

With the words of Gideon Lichfield, editor in chief of MIT Technology Review, “our annual list of 10 world-changing technologies invariably defies attempts to find an overarching theme. But a look back at the past few years shows a trend: we’re including more and more advances in artificial intelligence”.

There is no doubt that the AI will play - and is already playing - a huge role in the development of many aspects of our lives: the way we communicate and build relationships; how we work and find jobs; the strategy of businesses and organizations; how we take care of our health; the way Brands personalize the customer experience to appeal people's uniqueness.

3D METAL PRINTING

While 3D printing has been around for a while now, printing objects in other materials than plastic has been quite a dream (an expensive one). Now we are moving towards the ability to create large, intricate metal structures on demand; something that could revolutionize manufacturing, a new era for the 4.0 Enterprise.

ARTIFICIAL EMBRYOS

With the embryos, we face a topic hotly debated for its ethical and philosophical problems. And yet the research is moving faster than legislation and political debate. For the first time, researchers have made embryo-like structures from stem cells alone, without using egg or sperm cells, thus providing a new understanding of how life comes into existence.

SENSING CITY

For years we have heard about the smart city, but it is now time for an even smarter smart city. In Toronto, Alphabet’s Sidewalk Labs are already implementing sensors and analytics in order to rethink how we build and live cities. Sensing cities could make urban areas more affordable and citizen’s friendly.

AI FOR EVERYBODY

Artificial Intelligence is the next big thing in technology; there is no doubt about that. The only brake to its full application has always been the high costs of development. But now cloud-based AI is making the technology cheaper and easier to use, opening the market to many more companies.

DUELLING NEURAL NETWORKS

Right now the Artificial Intelligence can learn and identify things based on the processed data, but what if it could also have an ‘imagination’? Companies such as Google Brain, DeepMind and Nvidia are now matching two AI systems that can help each other to create original images, and generate something akin to a sense of imagination.

BABEL FISH EARBUDS

Google's omnipresence in this list shows that the company is not ‘just’ a search engine anymore. The Pixel Buds show the promise of near-real-time translation. The technology is still young and clunky, but it could help overcome the barrier of communication in an increasingly global world (in the wake of The Hitchhiker’s Guide to the Galaxy).

ZERO-CARBON NATURAL GAS

The brand new smart city requires a different approach to energy supply and distribution. The answer could be in a new approach to natural-gas plants, made to efficiently and cheaply capture carbon released by burning natural gas, thus avoiding greenhouse emissions.

PERFECT ONLINE PRIVACY

The most urgent issue of the digital era is the use (and abuse) of personal information. As shown by the Cambridge Analytica affair and the GDPR legislation, the road to the perfect online privacy is still long, but blockchain could help to make it faster. Computer scientists, in fact, are perfecting a cryptographic tool to carry out transactions without revealing any more information than necessary.

GENETIC FORTUNE-TELLING

Our destiny is written in our genes. This is science, not science fiction. The study of the genome can help scientists understand and predict diseases and human traits. DNA-based predictions could be the next significant public health advance, but will also pose an ethical problem. Will the next evolution of discrimination be based on genetics?

MATERIALS’ QUANTUM LEAP

What is the next step in the evolution of computing? Quantum computing seems to be the correct answer, as recently shown by the use of a quantum computer to model the electronic structure of a simple molecule. Understanding molecules will allow chemists to design more effective drugs and better materials, but the prospect of a new wave powerful computers comes with a question: What should (and could) we do with so much power?

Photo by Billy Huynh on Unsplash

Download The 7 Pillars Of The New Customer Loyalty to define the foundations on which to build your engagement and loyalty strategy, create innovative experiences and establish a lasting and valuable relationship with your customers.

Soft Skills are the New Core Skills – and Technology Can Hire Them

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Mark Murphy, the author of Hiring For Attitude, leadership trainer and CEO of Leadership IQ, has trained companies like Microsoft and IBM. In one of his research he tracked 20,000 new hires, and found that 46% of them failed within 18 months.

Even more shocking than the failure rate was the fact that 89% of the time it happened for attitudinal problems towards work and colleagues, and only 11% for lack of expertise. The attitudinal deficits included low levels of emotional intelligence, motivation, and temperament.

In today's fluid and interpersonal workplaces, skills such listening and learning from criticism, collaborating with others, working under pressure, presenting ideas effectively, and a having a positive, flexible attitude become all vital qualities for career success.

And while studying takes us on a path towards acquiring those hard, technical skills that we need to manage our job operationally, soft skills have little to do with knowledge or expertise. They are closely linked with our character.

As a combination of social competences, communication abilities, and emotional intelligence, soft skills are the spearhead of our inner nature and a direct result of our personal inclinations, which can strengthen or weaken them.

Some personality traits, in particular, have proven to be strong predictors of career success, leading to superior performances in general people’s working lives and within different jobs.

Let’s look at two important - yet not so well-known - personality traits: Internal Locus of Control, the key to success in any work environment; Need for Closure, which can have a different impact in various job functions.

Locus of Control

Locus of Control is our tendency to believe that 'control' resides internally within us, or externally, with others or the situation.

Individuals with an internal Locus of Control (called "internals") feel that they are in charge of their life and have primary responsibility for their actions, whether they are successes or failures.

Individuals with an external Locus of Control (called "externals") tend to feel more vulnerable and view themselves as victims of circumstances, fate, luck, and the influence of other people. They are more likely to make excuses or blame other people, events, or things, rather than taking responsibilities.

Having an internal Locus of Control is a source of energy, motivation, and confidence, which represents an advantage at all levels within an organization in many areas and situations. For example:

Effective Leadership. An "internal" leader is more likely to be favored by group members. One reason is that "internals" are perceived as more influential than "externals" because they take responsibility for events, emphasizing that they can change unfavorable conditions.

Taking the Initiative. Effective managers demonstrate a strong self-efficacy and an internal Locus of Control when they take steps to circumvent obstacles, actively seek information to solve problems, and usually initiate action, rather than waiting for things to happen.

Occupational Well-being. Amongst other things, Locus of Control is found to be a strong predictor of occupational health, and 'internal' employees show higher levels of job satisfaction and lower levels of job insecurity.

Need for Closure

Need for Closure (NFC) describes people's desire for a firm answer to a question or an issue and an aversion toward ambiguity.

A person with a high NFC prefers order and predictability and, in uncertain situations, tends to seek closure urgently. In contrast, a person with a low NFC tends to tolerate more, or even to look for the fluidity of uncertain situations.

In business and management, this personality trait has significant implications. For example:

Decision Making. Employees' level of NFC can serve as a useful criterion to select decision makers in organizations, by identifying the decision-making style that fits better with a job function. People with a high NFC prefer to think about black-or-white solutions and simplified dichotomization. They are more willing to make instant decisions, whereas people with a low need for closure prefer to postpone decisions and carry out a more in-depth evaluation, even if it takes extra time.

Leadership Behavior. Experimental findings have highlighted that individual differences in the desire to reduce uncertainty affect people's leadership style. For example, supervisors that are high on NFC tend to show an autocratic leadership and a preference for 'hard power' tactics of social influence, whereas 'soft power' tactics are those that managers with a low NFC value most.

Coping with Change. Because of their desire for stability and permanence, people with a high NFC feel uncomfortable with change. They are also more resistant to changing their minds and yielding to persuasion attempts. For example, high NFC levels are associated with political conservatism, an ideology whose core definition involves resistance to change.

Personality assessments have always been a common practice amongst large companies, to identify peoples' strengths and weaknesses and help HR managers decide whether or not an employee is a good organizational fit. To this end, traditional paper-based and web-based questionnaires are still today the primary tool used by companies.

Technology, however, is changing the face of the HR world by progressively, but rapidly, automating processes on previously unimaginable scales. Today's softwares can do much more than grade multiple-choice questions to measure people's technical skills.

With natural language processing and machine learning algorithms analyzing things like keywords, intonation, and body language, it becomes possible to capture more intangible human qualities. This data can then be used to create a psychological profile that allows HR managers to predict whether a person's attitudes fit with the company’s culture, values, and desired behaviors.

For the past year, the consumer-goods giant Unilever - for which about 170,000 employees work worldwide - has been using artificial intelligence to screen all its entry-level employees, and neuroscience-based games to measure their inherent traits. The company needed to renew itself, and transforming new talent recruitment by digitizing the first steps of the hiring process was a great way to do so, says Mike Clementi, VP of human resources for North America.

More and more, it has become clear that Artificial Intelligence not only improves the work processes of employees by automating time-consuming daily tasks; it is revolutionizing the HR world at all stages. Let’s look at some of them:

Hiring Process. By scanning resumes, machine learning algorithms can do initial screenings to identify the best candidates, eliminate unqualified prospects, and then create shortlists that can be organized based on specific skills, keywords or employment history.

Training Methods. By recording how an employee is responding to an ongoing training program, AI can help HR managers to better tailor future training sessions to each worker.

Performance Evaluation. By analyzing productivity data, AI can help to measure how well an employee is performing, thus becoming a supplemental tool to management decisions.

Turnover Prediction. By analyzing employee engagement data, gathered from quantitative surveys or qualitative methods, AI can determine an employee’s level of commitment or satisfaction, and better predict if he or she is at risk of leaving. That allows HR managers to decide whether to adopt some backup retention measures or provide new growth opportunities.

There have been great strides in the HR world, since technology was usually seen simply as a tool to streamline technical procedures. A turning point comes when AI applications are increasingly expanding from specific standardized, low cognitive demand tasks, to typically human jobs, such as discovering the human side of employees, from their temporary feelings and emotions to their stable personality traits.

We cannot predict the future of HR with a 100 percent certainty, but what we can see is undoubtedly a world where technology will embrace more and more the human side of people.

Photo by Larm Rmah on Unsplash

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What Are The Top Priorities When You Invest In Experience Strategy?

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Customer experience is one of the most used keywords to define what is happening to business in recent years. Today, the DCX is clearly a priority, but you need to understand how to invest in it to prevent it has not a real impact on your strategy.

What are the top priorities when you start investing in the creation of the best possible experience? Tricky question, especially if your objective is to deliver relevant, personalized experiences. One hint? Start with improving the relationship.

Over the last two years, we have repeatedly referred to a series of statistics by Gartner, still valid today: 89 percent of companies expect to compete mostly on the basis of customer experience, that will eventually overtake price and product as the primary pillar of differentiation between Brands.

By 2018, more than 50 percent of organizations will redirect their investments to customer experience innovations. Where should you start? One thing is sure: doing things the way you have always done them or relying on old marketing practices will not solve a completely new set of problems.

From the very first day Internet entered our houses and smartphone took its place in our hands, the experience has become “the experiences”. Digital technologies multiply the points of contact between a brand and a customer and disrupt the concepts of space and time when it comes to the buying process.

The DCX is the result of all interactions a customer has with your organization and its products or services over a specific period of time. The entirety of these different experiences defines the overall relationship, in terms of intensity, relevance, and duration.

When it comes to planning new investments, the focus is usually on the inside: policies, restrictions, roles, and everything that could put the sticks between the wheels. This is important to highlight some key relationships (i.e. with investors, employees, partners).

We know, however, that - in order to be successful - a marketing strategy must start with the customers, their journeys, and touchpoints. The ability to step into your customer's shoes and adopt a holistic approach to the experience strategy is essential to overcome the limitations of siloed departments.

Yet despite all the customer-centric statements and the alleged obsession for customers, few companies actually have a long-term vision that aligns the planning and management of the experience with a business strategy that connects the various departments into a coherent unity.

In a recent report, Altimeter unveiled this discrepancy between what the Brands think they are doing and what is ultimately perceived by the customer:

Experience is thus not about unicorns, rainbows, or soft fuzzy ideas. Instead, it is about a shared value proposition with customers that aligns to your business. (..) Experience is the mechanism through which your business strategy and brand value proposition are activated with customers.

A successful and relevant experience can happen only “when customer experience strategy focuses on and is measured by the strength and nature of the customer relationship. (...) In the end, you can only satisfy people if you deliver what it is that they want, at the time they want it, understanding what is relevant to them at that particular time and place.

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In digital markets, the most precious resource is time. The smartphone enables new behaviors and unlocks access to a persistent source of information. “That little device is enabling new ways of doing and learning things. It is helping us discover new ideas and new businesses. It is helping us manage our to-dos, tackle our problems, and inspire our plans.” (Google)

Anything can happen anytime, anywhere, and the Micro Moments have become the new battleground. Be There - Be Useful - Be Quick: this is the karma for the new era of customer relationships. If you do not show up, you lose. If you do not deliver contents relevant to the context, you lose. If you are slow and reject the changes, you are wasting resources.

So, now that you are all set and ready to invest in the future of your business, what are the areas you should focus on? We see a few priorities that define the quality of your next generation experience strategy.

CONTEXTUALIZATION

Customer experience defines the success of your organization. To build relationships that are relevant and drive sales, you must understand customers and connect with them on a personal basis. Understanding is the first step of the new marketing funnel, the ground where you build engagement and ongoing customer loyalty.

Today, there is no content without context. And the context implies not only the location or the devices used but also - and especially - the behavioral and emotional peculiarities of each customer. The psychographic profile will tell you everything you need to know to tailor experiences to the emotional preferences of your customers.

PREDICTION

To develop a strategy you always start by understanding where you and your customers already are. Digital technologies generate an enormous amount of data that you can use to extract relevant insights about your Brand and how you preside the touch points of the customer journey.

The Big Data, however, can be overwhelming. They are often too abstract and unrelated to the context. The relations in data are more important than the data itself, so you should adopt a ‘Small Data’ approach and leverage on technologies (i.e. machine learning) to predict the evolution of markets and the results of your investments.

AUTOMATION

The demand for faster responses generates the need for automation. The spread of smart connected machines makes it possible to automate every aspect of your organization, from the internal and productive processes to the relationship with stakeholders, employees, and customers.

The Internet of Things, fueled by the advancements in the Artificial Intelligence, creates a network of smart objects that can communicate without the human intervention. The future of manufacturing (Industry 4.0) and retail lies in the automation, and so does customer support (just think about chatbots, conversational interfaces, and the New Voice of Customer).

Now it is your turn. What are your top priorities? What are the pillars of your customer experience strategy?

Photos byTodd Diemer and Denys Nevozhai on Unsplash

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How Artificial Intelligence Is Disrupting Your Organization

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Whoever reads a science fiction novel ends up thinking about smart machines that can sense, learn, communicate and interact with human beings. The idea of Artificial Intelligence is not new, but there is a reason if big players like Google, Microsoft or Amazon are betting precisely on this technology right now.

After decades of broken promises, the AI is finally reaching its full potential. It has the power to disrupt your entire business. The question is: How can you harness this technology to shape the future of your organization?

Ever since the human has learned to dream, he has dreamed about ‘automata’, objects able to carry out complex actions automatically. The mythologies of many cultures - Ancient China and Greece, for example - are full of examples of mechanical servants.

Engineers and inventors in different ages attempted to build self-operating machines, resembling animals and humans. Then, in 1920, the Czech writer Karel Čapek used for the first time the term ‘Robot’ to indicate artificial automata.

The rest is history, with the continuing effort to take the final step from mechanical robots to intelligent machines. And here we are, talking about a market expected to reach over five billion dollars by 2020 (Markets & Markets).

The stream of news about the driverless cars, the Internet of Things, and the conversational agents is a clear evidence of the growing interest. Behind the obvious, though, we can find more profitable developments and implications for the Artificial Intelligence.

Back in 2015, while reporting our annual trip at the SXSW, we said that the future of the customer experience goes inevitably through the interconnection of smart objects.

The AI is a top choice when talking about the technologies that will revolutionize the retail store and the physical experience we have with places, products, and people.

The hyperconnected world we live in has a beating heart of chips, wires, and bytes. This is not a science fiction scenario anymore; this is what is happening, here and now, even when you do not see it.

The future of products and services appears more and more linked to the development of intelligent functions and features. Take a look at what has been done already with the embedded AI, that can enable your product to:

  • Communicate with the mobile connected ecosystem - Just think about what we can already do using Google Assistant on the smartphone, or the Amazon Alexa device.
  • Interact with other smart objects that surround us - The Internet of Things has completely changed the way we experience the retail store (and our home, with the domotics).
  • Assist the customer, handling a wider range of requests - The conversational interfaces, like Siri and the chatbots, act as a personal tutor embedded in the device.

As the years pass by, the gap between weak and strong AI widens increasingly. A theory revived by a recent report by Altimeter, not by chance titled “The Age of AI - How Artificial Intelligence Is Transforming Organizations”.

The difference can be defined in terms of the ability to take advantage of the data to learn and improve. Big data and machine learning, in fact, are the two prerequisites of the modern smart technology.

So, on the one hand, we have smart objects that can replace the humans on a specific use case - i.e. to free us from heavy and exhausting duties - but do not learn or evolve in time.

On the other hand, we have the strong AI, the most promising outlook: An intelligence so broad and strong that is able to replicate the general intelligence of human beings. It can mimic the way we think, act and communicate.

The “pure AI” is aspirational but - apart from the Blade Runner charm - this is the field where all the tech giants are willing to bet heavily. The development and implementation of intelligent machines will define the competitive advantage in the age of AI.

According to BCG, “structural flexibility and agility - for both man and machine - become imperative to address the rate and degree of change.” As you can see in the following graph, you should look at the AI through four lenses:

  • Customer needs
  • Technological advances
  • Data sources
  • Decomposition of processes

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First things first. It is important to incorporate the technological advances, gather the different data sources, and map the different processes involved. However, it is way more important to start from the basics, the customers.

Many types of research tend to focus on the tech-side of the moon but there is something you should never forget: everything starts with the customer. This is the pillar of every organization, and it is not going to change because of smart machines.

Know your customer” means that you must to understand their needs, desires, pain points, and behaviors. Your business potential lies in the acknowledgment of the centrality of people.

The AI is a tool, not the purpose. The ultimate purpose is to create the best customer experience, blending technology and emotions so that you can engage your customers, monetize the opportunities, and increase the relevance of your brand.

Everything is connected to the customer:

The opportunity (and risk) of AI is not just in a device that will play a song or order tickets to a concert. The value of systems based on machine learning is based on their ability to sense, communicate, learn, act, and adapt over time and to connect with other systems that do the same so that they can anticipate and act on a range of needs - be they related to medicine commerce, service and support, or customer experience.” (Altimeter - The Age of AI)

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Now that the boundaries between what is human and what is artificial blur, there is one last element that you should never forget. The relationship with your customers is grounded in trust.

Transparency (in the use of data, in the management of the real-time interactions) is essential to win the distrust when the distinctions between human, AI-assisted, and AI interactions could very well disappear.

As Pedro Domingos, the author of The Master Algorithm, once said that the “Artificial Intelligence is not so scary as it seems when it translates into artificial smartness.

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